In order to control and optimize a cell culture, different parameters (growth curves, consumption/depletion of nutrients, production/accumulation of by products (which are usually toxic), determination of physiological state) must be determined at various stages. However, some parameters of a cell culture are very difficult to quantify because there is only a limited number of methods for measuring them during laboratory experimentation or industrial production. Usually, samples of the cell cultures are analyzed with conventional techniques (filtration, drying, cell counting, HPLC). These methods are generally lengthy and costly processes and cannot be performed in real time. Consequently, in industrial settings, cells are generally cultured using a pre-established recipe, based on a statistic indicator which can be indirectly linked to a specific cellular state. This strategy does not accommodate real time optimization of cell cultures and results in important economic loss.
Some methods currently known in the art enable the determination of the biomass concentration in real time: probes measuring a NADH signal through the determination of its auto-fluorescence in cell culture, turbidity (such as the ASD19-N™ of Optek Danulat) and capacitance (such as the BIOMASS MONITOR™ of Aber Instruments Ltd.). However, it is virtually impossible to determine in real time other important parameters of the cell culture such as cellular proliferation, physiological state, consumption of nutrients, production of a by-product, etc
Even though the use of endogenous fluorescence to determine the status of a cell culture has proved to be difficult, some research teams have published their efforts toward the understanding of this subject. Hisiger et Jolicoeur (2005, Biotechnological Progress, 21, 580-589) found eight unknown fluorescent compounds in Eschscholtzia californica culture, as well as signal overlay of benzophenanthridic alkaloids and riboflavins. They also reported the relationship between NAD(P)H associated auto-fluorescence and cell activity in E. californica. They further reported the relation between NAD(P)H associated auto-fluorescence and biomass in C. roseus. Finally, they noted the relationship between NAD(P)H associated and riboflavin associated auto-fluorescence and growth rate in C. roseus. Applicant would like to note that the tryptophan and tryptamine signals were inverted in their studies. Applicant would also like to point out that the same indicator (NAD(P)H) was correlated to two physiological variables which are linearly independent. This conclusion was supported by only one reading which can lead to a erroneous interpretation of the real and reproducible correlations.
Hisiger and Jolicoeur (2005, Journal of Biotechnology, 117, 325-336) then reported the relationship between NAD(P)H, riboflavin and tryptophan-associated auto-fluorescence and biomass concentration in P. pastoris. They also suggested the relationship between riboflavin associated auto-fluorescence and biomass concentration in the NSO cell line. Surprisingly, even if riboflavin and tryptophan are not bio-synthesized by mammal cells, Hisiger and Jolicoeur pretend that it is <<possible>> to correlate the biomass concentration and the riboflavin-associated auto-fluorescence. However, Hisiger and Jolicoeur also add that “[ . . . ] the presence of some unidentified fluorescence signals that are overlapping the ones of interest [ . . . ] are limiting the applicability and the reliability of this type of probe”.
Schalger et al. (1996, Advanced Space Research, 18, 113-124) developed algorithms to track microbial population evolution of Pseudomonas aeroginosa by auto-fluorescence. Schalger et al. reported estimation errors up to 42.9%.
Asali et al. (1992, Biotechnology, 23, 83-94) reported the relationship between NAD(P)H associated fluorescence and biomass concentration in C. roseus. 
Farabegoli et al. (2003, Water Research, 37, 2732-2738) reported the relation between NAD(P)H associated auto-fluorescence and biomass concentration in active mud.
A relationship between NAD(P)H-associated auto-fluorescence and biomass concentration in C. botanica was also identified by Harrison et Chance (1970, Applied microbiology, 19, 446-450).
Horvath et al. (1993, Biotechnology progress, 9, 666-670) reported a relationship between tryptophan-associated auto-fluorescence and biomass concentration in S. cerevisae. 
Li et Humphrey (1991, Biotechnology and Bioengineering, 37, 1043-1049) reported the relationship between NAD(P)H, riboflavin, tryptophan and pyridoxine associated auto-fluorescence and biomass concentration in C. utilis. A relationship between tryptophan-associated auto-fluorescence and biomass concentration in S. cerevisae was also reported by this group.
Also in S. cerevisae, Lindemann et al. (1998, Sensors and actuators B, 51, 273-277) reported a relationship between riboflavin-associated auto-fluorescence and biomass concentration.
Palmer et al. (2003, Photochemistry and photobiology, 78, 5, 462-469) published the relationship between tryptophan-associated auto-fluoescence and cellular concentration in human mammal cells. Their correlation was not used to monitor cell culture, but to discriminate between malignant and normal phenotypes in different cell lines.
Scheper et al. (1987, Annals New York Academy of Sciences, 506, 431-445) reported the relationship between NAD(P)H-associated auto-fluorescence and cellular activity in various organisms.
Siano et Mutharasan (1989, Biotechnology and Bioengineering, 34, 660-670) published the relationship between NAD(P)H-associated auto-fluorescence and cellular activity in S. cerevisae. 
As shown herein, some endogenous auto-fluorescent markers have been suggested to be correlated with biomass concentration in some cell culture. However, information regarding to biomass concentration/accumulation provides only partial information about the cell culture. For example, it does not provide information about the metabolic behavior of the cells in culture (ex.: cell proliferation, nutriments consummation and use, metabolic activity, etc.).
In light of the above, it would be highly desirable to be provided with appropriate markers that are accurate representatives of one or several parameters of a cell culture. These markers should be rapidly measured in order to provide real time or quasi real time information on the status of the cell culture or to detect a cellular contamination. These markers should also be able to represent different culture parameters in order to provide very important information on the status of the cell culture.